Merge pull request #249537 from ssalgadodev/patch-32

prmerger-automator[bot] · web-flow · commit ce0c65ba9324 · 2023-09-05T01:41:12.000Z
Freshenss update/ Acrolinx changes tutorial-auto-train-image-models.md
diff --git a/articles/machine-learning/tutorial-auto-train-image-models.md b/articles/machine-learning/tutorial-auto-train-image-models.md
@@ -9,7 +9,7 @@ ms.topic: tutorial
 author: swatig007
 ms.author: swatig
 ms.reviewer: ssalgado
-ms.date: 05/26/2022
+ms.date: 08/26/2023
 ms.custom: devx-track-python, automl, event-tier1-build-2022, ignite-2022, devx-track-azurecli, build-2023
 ---
 
@@ -25,7 +25,7 @@ This object detection model identifies whether the image contains objects, such
 Automated ML accepts training data and configuration settings, and automatically iterates through combinations of different feature normalization/standardization methods, models, and hyperparameter settings to arrive at the best model.
 
 
-You'll write code using the Python SDK in this tutorial and learn the following tasks:
+You write code using the Python SDK in this tutorial and learn the following tasks:
 
 > [!div class="checklist"]
 > * Download and transform data
@@ -83,7 +83,7 @@ You'll write code using the Python SDK in this tutorial and learn the following
 
 You first need to set up a compute target to use for your automated ML model training. Automated ML models for image tasks require GPU SKUs.
 
-This tutorial uses the NCsv3-series (with V100 GPUs) as this type of compute target leverages multiple GPUs to speed up training. Additionally, you can set up multiple nodes to take advantage of parallelism when tuning hyperparameters for your model.
+This tutorial uses the NCsv3-series (with V100 GPUs) as this type of compute target uses multiple GPUs to speed up training. Additionally, you can set up multiple nodes to take advantage of parallelism when tuning hyperparameters for your model.
 
 The following code creates a GPU compute of size `Standard_NC24s_v3` with four nodes.
 
@@ -349,7 +349,7 @@ compute: azureml:gpu-cluster
 > [!IMPORTANT]
 > This feature is currently in public preview. This preview version is provided without a service-level agreement. Certain features might not be supported or might have constrained capabilities. For more information, see [Supplemental Terms of Use for Microsoft Azure Previews](https://azure.microsoft.com/support/legal/preview-supplemental-terms/).
 
-In your AutoML job, you can perform an automatic hyperparameter sweep in order to find the optimal model (we call this functionality AutoMode). You only specify the number of trials; the hyperparameter search space, sampling method and early termination policy are not needed. The system will automatically determine the region of the hyperparameter space to sweep based on the number of trials. A value between 10 and 20 will likely work well on many datasets.
+In your AutoML job, you can perform an automatic hyperparameter sweep in order to find the optimal model (we call this functionality AutoMode). You only specify the number of trials; the hyperparameter search space, sampling method and early termination policy aren't needed. The system will automatically determine the region of the hyperparameter space to sweep based on the number of trials. A value between 10 and 20 will likely work well on many datasets.
 
 # [Azure CLI](#tab/cli)
 
@@ -396,13 +396,13 @@ When you've configured your AutoML Job to the desired settings, you can submit t
 
 In your AutoML job, you can specify the model architectures by using `model_name` parameter and configure the settings to perform a hyperparameter sweep over a defined search space to find the optimal model.
 
-In this example, we will train an object detection model with `yolov5` and `fasterrcnn_resnet50_fpn`, both of which are pretrained on COCO, a large-scale object detection, segmentation, and captioning dataset that contains over thousands of labeled images with over 80 label categories.
+In this example, we'll train an object detection model with `yolov5` and `fasterrcnn_resnet50_fpn`, both of which are pretrained on COCO, a large-scale object detection, segmentation, and captioning dataset that contains over thousands of labeled images with over 80 label categories.
 
 You can perform a hyperparameter sweep over a defined search space to find the optimal model.
 
 #### Job limits
 
-You can control the resources spent on your AutoML Image training job by specifying the `timeout_minutes`, `max_trials` and the `max_concurrent_trials` for the job in limit settings. Please refer to [detailed description on Job Limits parameters](./how-to-auto-train-image-models.md#job-limits).
+You can control the resources spent on your AutoML Image training job by specifying the `timeout_minutes`, `max_trials` and the `max_concurrent_trials` for the job in limit settings. Refer to [detailed description on Job Limits parameters](./how-to-auto-train-image-models.md#job-limits).
 # [Azure CLI](#tab/cli)
 
 [!INCLUDE [cli v2](includes/machine-learning-cli-v2.md)]
@@ -420,11 +420,11 @@ limits:
 
 ---
 
-The following code defines the search space in preparation for the hyperparameter sweep for each defined architecture, `yolov5` and `fasterrcnn_resnet50_fpn`.  In the search space, specify the range of values for `learning_rate`, `optimizer`, `lr_scheduler`, etc., for AutoML to choose from as it attempts to generate a model with the optimal primary metric. If hyperparameter values are not specified, then default values are used for each architecture.
+The following code defines the search space in preparation for the hyperparameter sweep for each defined architecture, `yolov5` and `fasterrcnn_resnet50_fpn`.  In the search space, specify the range of values for `learning_rate`, `optimizer`, `lr_scheduler`, etc., for AutoML to choose from as it attempts to generate a model with the optimal primary metric. If hyperparameter values aren't specified, then default values are used for each architecture.
 
 For the tuning settings, use random sampling to pick samples from this parameter space by using the `random` sampling_algorithm. The job limits configured above, tells automated ML to try a total of 10 trials with these different samples, running two trials at a time on our compute target, which was set up using four nodes. The more parameters the search space has, the more trials you need to find optimal models.
 
-The Bandit early termination policy is also used. This policy terminates poor performing trials; that is, those trials that are not within 20% slack of the best performing trial, which significantly saves compute resources.
+The Bandit early termination policy is also used. This policy terminates poor performing trials; that is, those trials that aren't within 20% slack of the best performing trial, which significantly saves compute resources.
 
 # [Azure CLI](#tab/cli)
 
@@ -585,7 +585,7 @@ auth_mode: key
 
 ### Create the endpoint
 
-Using the `MLClient` created earlier, we'll now create the Endpoint in the workspace. This command will start the endpoint creation and return a confirmation response while the endpoint creation continues.
+Using the `MLClient` created earlier, we'll now create the Endpoint in the workspace. This command starts the endpoint creation and return a confirmation response while the endpoint creation continues.
 
 
 # [Azure CLI](#tab/cli)
@@ -605,7 +605,7 @@ We can also create a batch endpoint for batch inferencing on large volumes of da
 
 ### Configure online deployment
 
-A deployment is a set of resources required for hosting the model that does the actual inferencing. We'll create a deployment for our endpoint using the `ManagedOnlineDeployment` class. You can use either GPU or CPU VM SKUs for your deployment cluster.
+A deployment is a set of resources required for hosting the model that does the actual inferencing. We create a deployment for our endpoint using the `ManagedOnlineDeployment` class. You can use either GPU or CPU VM SKUs for your deployment cluster.
 
 
 # [Azure CLI](#tab/cli)
@@ -641,7 +641,7 @@ readiness_probe:
 
 ### Create the deployment
 
-Using the `MLClient` created earlier, we'll now create the deployment in the workspace. This command will start the deployment creation and return a confirmation response while the deployment creation continues.
+Using the `MLClient` created earlier, we'll create the deployment in the workspace. This command starts the deployment creation and return a confirmation response while the deployment creation continues.
 
 # [Azure CLI](#tab/cli)
 
@@ -658,7 +658,7 @@ az ml online-deployment create --file .\create_deployment.yml --workspace-name [
 ---
 
 ### Update traffic:
-By default the current deployment is set to receive 0% traffic. you can set the traffic percentage current deployment should receive. Sum of traffic percentages of all the deployments with one end point should not exceed 100%.
+By default the current deployment is set to receive 0% traffic. you can set the traffic percentage current deployment should receive. Sum of traffic percentages of all the deployments with one end point shouldn't exceed 100%.
 
 # [Azure CLI](#tab/cli)
 
@@ -710,7 +710,7 @@ CLI example not available, please use Python SDK.
 
 ## Clean up resources
 
-Do not complete this section if you plan on running other Azure Machine Learning tutorials.
+Don't complete this section if you plan on running other Azure Machine Learning tutorials.
 
 If you don't plan to use the resources you created, delete them, so you don't incur any charges.
 
@@ -742,12 +742,12 @@ In this automated machine learning tutorial, you did the following tasks:
     # [Azure CLI](#tab/cli)
     [!INCLUDE [cli v2](includes/machine-learning-cli-v2.md)]
     
-    * Review detailed code examples and use cases in the [azureml-examples repository for automated machine learning samples](https://github.com/Azure/azureml-examples/tree/sdk-preview/cli/jobs/automl-standalone-jobs). Please check the folders with 'cli-automl-image-' prefix for samples specific to building computer vision models.
+    * Review detailed code examples and use cases in the [azureml-examples repository for automated machine learning samples](https://github.com/Azure/azureml-examples/tree/sdk-preview/cli/jobs/automl-standalone-jobs). Check the folders with 'cli-automl-image-' prefix for samples specific to building computer vision models.
     
     # [Python SDK](#tab/python)
     [!INCLUDE [sdk v2](includes/machine-learning-sdk-v2.md)]
 
-    * Review detailed code examples and use cases in the [GitHub notebook repository for automated machine learning samples](https://github.com/Azure/azureml-examples/tree/main/sdk/python/jobs/automl-standalone-jobs). Please check the folders with 'automl-image-' prefix for samples specific to building computer vision models.
+    * Review detailed code examples and use cases in the [GitHub notebook repository for automated machine learning samples](https://github.com/Azure/azureml-examples/tree/main/sdk/python/jobs/automl-standalone-jobs). Check the folders with 'automl-image-' prefix for samples specific to building computer vision models.
     
     ---
 
